Robust Positioning Systems in the Presence of Outliers Under Weak GPS Signal Conditions
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Bibliographic record
Abstract
In this paper, two epoch-by-epoch robust positioning techniques for global positioning system (GPS) are proposed to deal with the problem of positioning in weak signal conditions in which the probability of outlier in signal acquisition is larger than zero. We propose to accept outliers into the positioning algorithm, however, in this case either robust estimation or outlier detection must be used to overcome the devastating effect of such outliers on traditional positioning algorithms. In order to improve the sensitivity of a GPS receiver, we propose to use novel methods that are able to deal with the problem of estimating the position of a receiver based on pseudo-ranging measurements that are contaminated by outliers. Simulations are carried out to demonstrate the robustness of the proposed techniques in terms of success rate of the algorithms in finding the correct solution, when there are a different number of outliers in ranging measurements from satellites.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it